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Each row represents a state-year in the panel. |\n| Policy | Continuous, standardized index summarizing the ideological orientation of a state\u2019s policy outputs in that year. The score is estimated from nearly 150 statutory and regulatory indicators spanning domains such as social welfare, taxation, labor, civil rights, women\u2019s rights, morality laws, family planning, environment, religion, criminal justice, and drug policy. Higher values indicate a more liberal policy orientation. The scale is standardized to be comparable across states and years (centered roughly at 0 with a typical standard deviation near 1). |\n| sen_dem_control | Indicator (0/1) that the Democratic Party held a majority of seats in the state senate (upper chamber) in that year, based on official election returns and chamber composition. For Nebraska\u2019s nonpartisan unicameral legislature, this variable is generally coded as missing. |\n| hs_dem_control | Indicator (0/1) that the Democratic Party held a majority of seats in the state house/lower chamber in that year, based on official election returns and chamber composition. For Nebraska, typically coded as missing due to nonpartisan unicameral structure. |\n| Policy_L1 | One-year lag of the policy index (value from t\u22121). Included to capture persistence and gradualism in state policy change over time. |\n| Policy_L2 | Two-year lag of the policy index (value from t\u22122). Provides an additional look-back to characterize medium-run policy momentum. |\n| South11 | Indicator (0/1) for membership in the traditional 11-state U.S. South: AL, AR, FL, GA, LA, MS, NC, SC, TN, TX, VA. Useful for distinguishing long-standing regional political and institutional contexts. |\n| gov_dem | Indicator (0/1) that the sitting governor was affiliated with the Democratic Party for the relevant portion of the calendar year, as compiled from official administrative records and election data. |\n| gov_dem_X_hs_dem | Product term (0/1) equal to 1 when both gov_dem = 1 and hs_dem_control = 1 in the same year; 0 otherwise. Encodes concurrent Democratic control of the governorship and the lower chamber. |\n| gov_dem_X_sen_dem | Product term (0/1) equal to 1 when both gov_dem = 1 and sen_dem_control = 1 in the same year; 0 otherwise. Encodes concurrent Democratic control of the governorship and the upper chamber. |\n| both_dem | Indicator (0/1) that Democrats controlled both legislative chambers in that year (hs_dem_control = 1 and sen_dem_control = 1). |\n| full_dem | Indicator (0/1) that Democrats held a \u201ctrifecta\u201d in that year: governorship plus majorities in both legislative chambers. |\n| state | Numeric state identifier for the panel unit. Codes map to U.S. states included in the panel; Nebraska\u2019s unicameral, nonpartisan legislature is handled specially for chamber-control variables. |\n\nDataset Context:\nThis dataset is an annual, state-year panel that tracks the ideological orientation of U.S. state policies alongside the party composition of key state institutions from the mid-1930s through the early 2010s. The central measure is a dynamic policy index that pools information from nearly 150 codings of enacted state statutes and regulatory provisions. The underlying source material includes historical statutory compilations, administrative records, and archival policy datasets, covering areas such as safety-net programs, taxation structures, labor and employment regulations, civil and voting rights, women\u2019s rights, morality and family policy, environmental regulation, religion in public institutions, criminal justice, and drug laws. Because most policy indicators are dichotomous (present/absent) and some are continuous (e.g., benefit levels), the index is estimated using a modeling approach that accommodates mixed data types and allows comparability over time. Scores are standardized so that higher values represent more liberal policy bundles, facilitating interpretation across decades when the national policy environment shifted.\n\nParty control variables are constructed from state election returns and official chamber rosters, identifying which party held the governor\u2019s office and whether Democrats held majorities in the state house and senate in each year. Nebraska\u2019s unique nonpartisan unicameral legislature is typically excluded from chamber-majority coding or marked as missing for those fields. The dataset also includes composite indicators for concurrent control across branches (e.g., both chambers, or governorship plus both chambers) and simple product terms that register simultaneous control by party across institutions.\n\nCoverage spans 1936\u20132014, a period that includes major institutional realignments, changes in party competition, and significant waves of policy innovation at the state level. The inclusion of lagged versions of the policy index reflects the empirical regularity that state policy changes are often incremental and path dependent. A regional flag for the traditional 11-state South is provided to facilitate analyses that account for historically distinct political and legal environments in that region. 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Conditioning on a collider opens non-causal paths and induces selection bias. The guidance warns that conditioning on colliders or post-treatment variables introduces spurious associations: \u201cconditioning on the post-treatment variable introduces spurious correlations\u2026 collider bias\u201d and in the M-structure \u201cwhen conditioning on Z, we introduce collider bias\u201d (Applied Causal Inference, Sec. 11.3, pp. 308\u2013311). Likewise, gov_dem_X_hs_dem and gov_dem_X_sen_dem are deterministic descendants/functions of the treatment (products of gov_dem with chamber control). The backdoor criterion requires that \u201cNo element of S is a descendant of D\u201d (Sec. 11.1, p. 300), and \u201cWe should not condition on M, because it is a descendant of D\u201d (Sec. 11.2, p. 301). Controlling for these interaction terms would violate that rule and change the estimand to a controlled-direct-effect type object rather than the total effect.","reason_for_minimal_controlling_set":"The paper\u2019s panel-identification argument explicitly motivates controlling for lagged outcomes to absorb within-state trends that threaten identification under simple two-way fixed effects: \u201cThe crucial identifying assumption in the panel analysis is that the statistical model characterizes the counterfactual outcome each state would have exhibited under a different treatment assignment\u201d (p. 1350). They note that if only state and year shocks mattered, two-way FE would suffice, but \u201cthis assumption is unlikely to hold\u2026 We therefore estimate dynamic panel models with two-way FEs and lagged values of our dependent variable\u201d (pp. 1350\u20131351). Empirically, \u201cThe estimates shrink dramatically\u2026 under [the] FE\u2011LDV model with two lagged terms\u2026 [which] strongly suggests that FEs alone do not adequately account for within-state trends in Policy Liberalism\u201d (p. 1351). Thus, conditioning on Policy_L1 and Policy_L2 (pre\u2011treatment lags) is the minimal sufficient set among available controls to satisfy a conditional parallel trends/exogeneity assumption in this setting.","strategy":"Difference-in-Differences","time_variable":"year","treatments":["gov_dem"]},"identification_strategy":"Difference-in-Differences"},"headline_keys":{"infer.ols":["coef.TreatGroup_Post"]},"item_caveats":[{"kind":"implicit_window","note":"the question leaves implicit the comparison years the gold's script uses (pre year 1970)"}],"naive":{"dataset":"ds_0002","treatment":"gov_dem","y":"Policy"},"native_id":"3","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"cluster":"state","controls":["TreatGroup","Post","Policy_L1","Policy_L2"],"dataset":"ref_panel","x":["TreatGroup_Post"],"y":"Policy"},"tool":"infer.ols","why":"smf.ols('Policy ~ TreatGroup*Post + Policy_L1 + Policy_L2'), cluster by state"}]},"reference_path":{"calls":[{"args":{"cluster":"state","controls":["TreatGroup","Post","Policy_L1","Policy_L2"],"dataset":"ref_panel","x":["TreatGroup_Post"],"y":"Policy"},"tool":"infer.ols"}],"needs":[],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_g0","ref_g1","ref_panel"],"source_id":"crb_papers.q003.script"}},"question":"How much did state policy liberalism change, by the governor's first year in office, in states that elected a Democratic governor in 1971 relative to comparable states that remained under Republican governors?","requires":[],"runner":null,"sources":["crb_papers.q003.gold","crb_papers.q003.script"],"split":"public","suite":"crb_papers","tags":["econ-bench","external","crb","did","ref_benchmark_script"],"target":"agent","tool":null,"truth":"CausalReasoningBenchmark gold at the pinned revision: causal_queries.json effect and standard_error (the value the item's estimation.py prints), scored as evaluate_result.py scores estimate_within_solution_ci"}